AIMC Topic: Mutation
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Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.
Laboratory investigation; a journal of technical methods and pathology
Jun 11, 2024
Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In ...
GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoi...
Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.
Frontiers in immunology
May 28, 2024
INTRODUCTION: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially wit...
Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation.
PloS one
May 24, 2024
Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both ex...
Respiratory Syncytial Virus Vaccine Design Using Structure-Based Machine-Learning Models.
Viruses
May 22, 2024
When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection or reverse-genetic manipulation and may include point mutations, codon and gene deletions, and geno...
Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.
La Radiologia medica
May 18, 2024
PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases.
Machine learning analysis of gene expression reveals TP53 Mutant-like AML with wild type TP53 and poor prognosis.
Blood cancer journal
May 14, 2024
Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.
PloS one
May 14, 2024
To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combin...
Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 12, 2024
BACKGROUND: The KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to ...
Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 10, 2024
BACKGROUND AND PURPOSE: We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients.
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